US6061088A - System and method for multi-resolution background adaptation - Google Patents
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/254—Analysis of motion involving subtraction of images
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- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- the present invention generally relates to a system and method for updating background image information of a viewing area, and more specifically, to a multi-resolution background adaptation system and method.
- background adaptation In conventional background adaptation schemes, adaptation of a background image occurs when no activity in an entire video frame is detected.
- One well known approach to background adaptation is frame averaging. In this prior art method, an average of a number of input image frames is computed and used as the background image. Background adaptation based on averaging methods provide only a crude estimate of the background and do not consider the presence of moving objects in the scene. In addition, averaging is not applicable to situations in which people or objects stay in a scene for a long period of time. Moreover, averaging techniques are computationally expensive because they involve computing an average for each pixel for a plurality of image frames.
- the present invention is directed to a system and method for background adaptation of image information representing a scene or viewing area monitored by a video camera.
- a region or portion of a video frame is selected for analysis.
- the image information for smaller subregions within that region are analyzed to determine whether any subregion is substantially occluded by an object. If a portion of the region (at least one of the subregions) is substantially occluded by an object, then the background image frame is updated for the region. If none of the subregions is substantially occluded by an object, then each of the subregions is analyzed individually in the same manner. This process is repeated recursively for smaller and smaller regions.
- FIG. 1 is a block diagram showing the basic hardware components of the background adaptation system according to the present invention.
- FIG. 2 is a block diagram showing use of the background adaptation system with a tracking system.
- FIG. 3 is a graphical representation showing how a video frame is partitioned into multi-resolution cell levels in accordance with the present invention.
- FIG. 4 is a block diagram showing the software modules of the background adaptation system according to the present invention.
- FIG. 5 is a flow chart showing the operation of an update control module for controlling at what frequency of video frames background adaptation is performed.
- FIG. 6 is a flow chart showing the steps of the frame update background module.
- FIGS. 7-9 illustrate a flow chart for the steps of the multi-resolution background update module according to the present invention.
- FIG. 10 is a diagram showing an example of multi-resolution background adaptation.
- FIG. 11 is a flow chart showing the operation of the motion status module.
- the hardware components of the background adaptation system 100 are standard off-the-shelf components.
- the primary components in the system are one or more video cameras 110, one or more frame grabbers 120, and a processor 130, such as a personal computer (PC), having a memory 135 which stores software programs for controlling the processor 130.
- the combination of the video camera 10 and frame grabber 120 may collectively be referred to as an "image acquisition module" 145.
- the frame grabber 120 receives a standard video signal output by the video camera 110, such as a RS-170, NTSC, CCIR, or PAL video signal, which can be monochrome or color.
- the video camera(s) 110 are mounted or positioned to view a selected viewing area or scene 150 of interest, such as a checkout lane in a retail establishment, an automated teller machine (ATM), an entrance, an exit, or any other localized area where people may move and/or interact with devices or other people.
- a selected viewing area or scene 150 of interest such as a checkout lane in a retail establishment, an automated teller machine (ATM), an entrance, an exit, or any other localized area where people may move and/or interact with devices or other people.
- ATM automated teller machine
- the frame grabber 120 is embodied, for example, by a MeteorTM Color Frame Grabber, available from Matrox.
- the frame grabber 120 operates to convert the analog video signal into a sequence or stream of digital video frame images that are stored within the memory 135, and processed by the processor 130.
- the frame grabber 120 converts the video signal into a 320 ⁇ 240 NTSC) or 768 ⁇ 576 (PAL) color image, or in general a W ⁇ L image defining a single video frame of video information.
- PAL 768 ⁇ 576
- W ⁇ L image defining a single video frame of video information.
- Each pixel of a video frame has a predetermined bit resolution, such as 8 bits, and color data may be used to increase system performance.
- the background adaptation functionality is implemented by a background adaptation system program 200 stored in the memory 135 of processor 130.
- the background adaptation system program 200 may be installed in the memory 135 from another memory/storage medium, such as a CD-ROM, floppy disk(s), hard disk, etc., or it may be downloaded from an Internet site, or from an on-line service for installation into the memory 135.
- the background adaptation program 200 comprises a plurality of executable instructions which, when stored in the memory 135, cause the processor 130 to the processes depicted in FIGS. 5-11.
- the background adaptation functionality could be implemented by one or more application specific integrated circuits, a digital signal processor or other suitable signal processing architectures.
- the background adaptation system program 200 comprises several software modules, as will be explained hereinafter. Background image information generated by the background adaptation system program 200 is supplied to, for example, a tracking system 210 to track people, objects, etc., within the viewing area 150 of the video camera 110.
- the tracking system program 210 includes software modules which supply certain information to the background adaptation system program 200 that are used to determine background information, as will be explained hereinafter.
- the video frame 300 comprises a predetermined number of pixels, W ⁇ H, and is partitioned into different cell resolutions called "cell levels".
- the entire frame is the highest cell level and is called Level 0, and is W 0 ⁇ H 0 pixels in resolution, corresponding to the pixel resolution of one video frame.
- Each cell level is further partitioned into a plurality of constituent or "children" cells at the next lower cell level.
- Level 0 has 4 (four) children cells at Level 1; Level 1 is W 1 ⁇ H 1 and each Level 1 cell has 4 children cells at Level 2; Level 2 is W 2 ⁇ H 2 and each Level 2 cell has 4 children cells at Level 3; Level 3 is W 3 ⁇ H 3 and each Level 3 cell has 4 children cells at Level 4.
- Level 4 is W 4 ⁇ H 4 and is the lowest level and thus the Level 4 cells do not have children cells.
- the number of cell levels and the number of children cells for each cell is variable depending on a particular application.
- the term "children” cells used hereinafter means the direct children cells of a particular cell.
- the terms “descendants” or “descendant cells” means all of the constituent cells within the boundaries of a particular cell down to the lowest cell level.
- the resolution increases from cell Level 0 to cell Level 4.
- the background adaptation system program generates and operates on various parameters associated with each cell, some of which are constant and others which are dynamic.
- Width The width of the cell.
- NumBlobPixelsOverlap The number of pixels of the cell overlapped or occupied by image blobs representing an object or objects.
- BlobOverlapFrac The fraction of pixels in a cell overlapped or occupied by image blobs divided by the total number of pixels in the cell.
- BGDiffPixCount The number of pixels in the cell of a current video frame that are different from the corresponding cell in the background image frame.
- BGDiffFrac The fraction of pixels different from the corresponding cell in the background image frame divided by the total number of pixels for the cell.
- RetainStartTime The time at which the cell in the reference frame was last changed.
- LastStaticSceneTime The last time the image information for the current cell did not exhibit motion.
- the background adaptation system program 200 is closely associated with an image segmentation module 350.
- the image (or region) segmentation module 350 uses background image information generated by the background adaptation system program 200 to extract regions of an image frame in the scene or viewing area that are likely to correspond to objects or people, and which are not part of the background image.
- the image segmentation module 350 generates two pieces of information used by the background adaptation system program: image region or blob information potentially representing objects, people, etc., in the scene with respect to the background image, and a background difference frame representing a difference between a current video frame and the background image.
- the current video frame is supplied from the video acquisition module 145.
- the background adaptation system program 200 generates and updates the background image frame (hereinafter referred to as the background image) and generates a background edges image, both of which are used by the image segmentation module 350 to extract image blobs, etc.
- the background adaptation system program 200 is useful in a system that tracks persons in a scene, such as customer movement in a retail store, bank, etc.
- the background adaptation system program 200 comprises a frame background update module 400 and a multi-resolution background update module 500.
- the frame background update module 400 performs frame-level background adaptation processing and determines when multi-resolution background update operations should be performed.
- An update control module 370 controls the frequency at which the background adaptation function is performed.
- the operations of the multi-resolution background update module 500 are called by the frame background update module 400.
- Both the frame background update module 400 a nd the multi-resolution background update module 500 call upon the functions of several other software modules shown in the bottom half of FIG. 4.
- These other software modules include a region overlap analysis module 600, a motion status module 700, a step preparation module 800, a slow update module 810, a snap update module 820, an update edges module 830, a max child overlap module 840, an oldest static scene time module 850, a child static time stamp update module 860, a child time stamp update module 870 and an oldest child static scene time module 880.
- the function of the background adaptation system program 200 is to update the contents of the background image frame.
- the background image frame is a frame of video image information which represents the current background of the scene or viewing area.
- the snap update module 820 copies the content, that is, the information, for a particular cell of a current video frame into a corresponding cell in the background image frame, causing an immediate update of the background image frame with the contents of that cell.
- the slow update module 810 gradually changes the image information for a cell in the background frame toward that of the current cell over several background update cycles. This may involve increasing or decreasing the color planes of the image information inside the current cell by a step value and is performed for every pixel of the current cell, and may take several background update cycles depending on a step size.
- the value of the step size affects the rate of adaptation, where the smaller the step size, the slower the adaptation.
- the step size value is generated by the step preparation module 800.
- the parameters for adaptation depend on time rather than any processor dependent variable.
- pixel values can be changed only in discrete levels or increments.
- the step preparation module 800 is called once per frame and outputs step values by which a pixel is to be changed when the slow update module 810 is called, using SlowStepSize or VSlowStepSize.
- a desired step size R N is computed by multiplying the time difference between the current time TS and the time of the previous update cycle TS(k-1).
- the update edges module 830 updates the transitions or contrast gradients of the background image frame.
- a background edges image is created as output by the update edges module 830 and supplied as input to the image segmentation module 350.
- step 372 a counter called update count is incremented for each new video frame.
- step 374 the update count is compared with a user-programmable quantity called update period, and if the update count is an exact multiple of the update period, then the procedure continues. If not, the process returns to step 372.
- step 376 it is determined whether the reference frame has been initialized for the first video frame in the operation of the background adaptation system program. If it has not, then in step 378, the contents of the current video frame is copied into the reference frame, and in step 380, the update edges module is called to update the edges information for the background image frame.
- step 382 the frame background update module 400 is called to begin background adaptation processing on the current video frame.
- the background update cycles occur at a predetermined frequency of video frames depending on the value assigned to the update period quantity.
- the image information of the background image frame updated each background update cycle is stored in the memory 135, for example, and is coupled to the image segmentation module 350, as described above.
- the frame background update module 400 will be described. This module is called at the start of a background update cycle.
- step 410 for the current video frame, the image blob information, the background difference frame, the current video frame, and the current time stamp TS of the current frame are obtained from the image segmentation module 350 and from the stream of video frames supply by the video acquisition module.
- step 415 if it is the first time that the frame background update function is called, the RetainStartTime parameter is set to TS in step 417.
- a parameter called the previous time stamp, corresponding to TS(k-1) above is set equal to TS.
- step 420 the region overlap analysis module 600 determines, for all of the cells in all resolution levels, the number of pixels having values greater than a predetermined threshold indicating that they are occupied by image blobs based upon the image region or blob information supplied by the image segmentation module. This information is represented by NumBlobPixelsOverlap, and the fraction of the number of pixels occupied by image blobs relative to the total number of pixels in each cell is called BlobOverlapFrac.
- step 425 a test is made to determine whether there is a substantial change in the scene.
- the parameter BlobOverlapFrac for the entire video frame (Level 0) is compared with a threshold called SceneChangeThreshold. If the BlobOverlapFrac is greater than the SceneChangeThreshold indicating a significant sudden or abrupt scene change has occurred since the last background update cycle, then in step 430, a comprehensive update is performed to adjust the background image frame for this sudden scene change.
- the comprehensive update includes: (1) setting the RetainStartTime parameter equal to the current time stamp TS for all cells using the Child Time Stamp Update module 870; (2) setting the LastStaticSceneTime parameter to TS for all cells using the Child Static Time Stamp Update module 860; (3) calling the snap update module 820 to copy the image information for the entire current frame into the background image frame; and (4) calling the update edges module 830. If, on the other hand, the BlobOverlapFrac for the Level 0 cell is not greater than the SceneChangeThreshold, then the multi-resolution background update module 500 is called in step 435.
- the multi-resolution background update module starts with the largest cell (Level 0 cell) and continues to greater resolutions at the lower cell levels only if the lower cell levels are not substantially occluded by objects.
- the multi-resolution background update module 500 the operation of the multi-resolution background update module 500 will be described.
- the goal of the multi-resolution background update module 500 is to perform multi-resolution background adaptation to update the background image frame with image information at the smallest possible cell size.
- the expression "flooded by image blobs" means that the region under analysis is substantially occluded by an object or objects, and this test is performed by comparing a pixel overlap quantity for the cell with a threshold.
- the larger cell in the image on the right has at least one child cell (shown in the image on the left) which is flooded by image blobs. It would not prove useful to update the background image at a cell level lower than that of the larger cell (shown on the right) because at least one child cell at the lower level is so occluded by the presence of the person, it would be difficult to detect motion of an object within it. Consequently, the background image frame will not be updated any lower than the cell level shown in the image on the right.
- the general processing flow of the multi-resolution background update module is as follows. A region or portion of a video frame is selected for analysis. The image information for smaller subregions within that region are analyzed to determine whether any subregion is substantially occluded by an object. If a portion of the region (at least one of the subregions) is substantially occluded by an object, then the background image frame is updated for the region and the subregions are not analyzed individually. If none of the subregions is substantially occluded by an object, then each of the subregions is analyzed individually in the same manner.
- step 510 it is determined whether any direct child cell of the current cell is flooded with image blobs, that is, whether any children cell is substantially occluded by an object or objects.
- the max child overlap module 840 is called which gets the blob overlap fraction (BlobOverlapFrac) for each of the 4 direct children cells and returns the greatest BlobOverlapFrac of the 4 children cells, called MaxChildOverlap.
- MaxChildOverlap The value of MaxChildOverlap is compared with a flood threshold. If MaxChildOverlap is greater than the flood threshold, then it is declared that at least one of the children cells is flooded with image blobs.
- the parameter NumChildren for the current cell is checked to determine whether it is zero, indicating that the current cell is a cell in the lowest level.
- step 510 If in step 510 neither NumChildren is zero nor MaxChildOverlap is greater than the flood threshold for the current cell, then the process continues to step 512, where the RetainStartTime parameter is set to the current time stamp value TS. Then, in step 514, the multi-resolution background update module 500 is called for each of the direct children cells of the current cell to analyze each children cell individually. This occurs because in step 510, it has been determined that the children cells of the current cell are not flooded by image blobs, thus each of the children cells of the current cell should be analyzed individually to determine background image information at a lower cell level, corresponding to a greater resolution.
- the BlobOverlapFrac for the entire current cell is compared with a blob threshold.
- the blob threshold is set to be rather small so that the cell is considered to have image blobs in it even if it is a small amount.
- the image information for the current cell itself is analyzed to determine whether at least some part of the cell, such as 5% of it, is overlapped or covered by image blobs.
- step 516 it is determined whether the cell is sufficiently (at least partially) occluded by an object or objects, which is to be distinguished from the test in step 510 that determines whether any children cell of the current cell is substantially (nearly completely) occluded by an object or objects.
- One of two paths are taken depending on whether the current cell itself is determined to be at least partially overlapped by image blobs.
- the presence of image blobs in a cell is an indication not to update the background image frame with image information for that cell since the image blobs in that cell occlude the view of the background.
- the background update would be deferred to a later time, when the object causing the image blob has moved out of the cell.
- an object will remain in the scene sufficiently long without any motion. This condition is detected and the background image frame is updated accordingly, as will be explained hereinafter,.
- step 518 it is determined whether NumChildren is zero for the current cell, that is, whether the current cell is in the lowest cell level. If it is not, then in step 520, the oldest child static scene time module 880 is called. This module checks the LastStaticSceneTime of all of the descendants of the current cell, and returns the oldest one of all of those times. The LastStaticSceneTime parameter for the current cell is assigned this value. By determining when any of its children cells were inactive, the last time that the current cell was inactive is accurately determined.
- step 522 the motion status module 700 is called to determine whether there is motion in the current cell.
- the motion status module 700 will be described in more detail hereinafter. It returns one of three values, MOTION, NO MOTION, or PENDING. If MOTION is the value returned by the motion status module 700, then no further analysis is performed on the current cell for the current background update cycle. The image information in the background image corresponding to the cell is maintained unchanged.
- step 524 the next course of action to take in step 524 is to call the snap update module 820 and the update edges module 830 for the current cell.
- the snap update module updates the background image frame with image information for the current cell.
- step 526 since it was determined that there was no activity in the current cell, the LastStaticSceneTime parameter for the cell is updated to the current time TS, and the child static time stamp update module 860 is called to do the same for all descendant cells of the current cell.
- step 528 it is determined whether an image blob is detected inside the current cell which has been moving for a very long period of time by comparing the difference of the current time stamp TS and the LastStaticSceneTime with a predetermined multiple of the RetentionThreshold (4 times the RetentionThreshold as an example). If the difference is greater than 4 times the RetentionThreshold, then in step 530, the slow update module 810 and the update edges module 830 are called.
- the slow update module 810 is called with the VSlowStepSize parameter in step 530 to gradually update the background image frame with image information for the current cell.
- the test of step 528 is made when a PENDING status is determined instead of a MOTION status because a slow update is less likely to incorrectly adapt the background image towards a moving object, and more likely to adapt the background image for static parts of a stationary scene undergoing some sort of regular motion. This is particularly useful when there is a slowly moving object in front of a stationary object, where it is desirable to account for the slowly moving object in the background image under certain conditions.
- the background image frame is updated based upon image information for the current cell.
- the RetainStartTime for the cell is set to the current time TS and the same is done for all of the descendant cells of the current cell using the child time stamp update module 870.
- the LastStaticSceneTime is also set to the current time TS, for the current cell as well as all of its descendant cells using the child static time stamp update module 860 in step 534.
- step 536 a check is made of the corresponding cell in the background difference frame. Even when no image blobs are in the scene within the current cell, there may be some differences that have informational value, such as shadowing, noise, or imperfect image blob segmentation.
- BGDiffPixCount the number of pixels that are different between the current cell and the corresponding cell in the background image frame is determined.
- a fraction value BGDiffFrac is computed by dividing BGDiffPixCount by the total number of pixels in the cell (width ⁇ height of the cell). Then, in step 540, the fraction value BGDiffFrac is compared with a background difference threshold BGDiffrhreshold.
- step 542 the snap update module 820 is called to update the image information for the current cell of the current frame into the background image frame, and the update edges module 830 is also called to update the edges information for that cell. If the BGDiffFrac is greater than the BGDiffThreshold, then since the difference can be due to noise, shadowing or imperfect image segmentation, in step 544, the slow update module 810 is called, preferably with the SlowStepSize parameter, to track fast changes in illumination. Also, the update edges module 830 is called in step 544. Alternatively, in step 542, the snap update and update edges modules are called only if the BGDiffFrac is greater than 1/2 of the BGDiffThreshold, thus reducing computation without significant change in performance.
- the motion status module 700 is described in greater detail with reference to FIG. 11.
- the motion status module is based upon historical information concerning motion status in a cell. This historical information is stored in a reference frame. That is, the reference frame stores image information for cells in which during a background cycle it is determined that motion has occurred or has not occurred. The image information in the reference frame is used in subsequent background update cycles to determine whether motion has occurred, has not occurred or is pending within the cell.
- the motion status module begins by getting the intensity planes of the reference frame and the current frame for the current cell in step 702.
- the intensity planes comprise information representing the gray or monochromatic intensity information for a video frame.
- the intensity planes for the current frame and the reference frame are compared with each other within the current cell to count the number of pixels which are significantly different, that is, different by a predetermined amount represented by an amplitude threshold. Each corresponding pixel that is different between the reference frame and the current frame by a predetermined amount is counted as significantly different.
- the total number of different pixels is divided by NumBlobPixelsOverlap in the current cell.
- the result is compared with a percent threshold. If it is larger than the percent threshold, a quantity DIF is set to TRUE, and if it is not larger than the percent threshold, then DIF is set to FALSE. Other methods for setting DIF are also possible, including using color information.
- step 708 the image information for the current cell in the current frame is copied into the corresponding cell of the reference frame.
- the RetainStartTime parameter is set equal to the current time TS for the current cell and for its descendant cells.
- step 712 the MOTION status is returned for use accordingly as shown in FIG. 8.
- step 714 a test is made to determine whether the scene inside the current cell has not changed for a predetermined period of time corresponding to a RetentionThreshold. If the difference between the current time stamp TS and RetainStartTime is longer than the RetentionThreshold, then it is determined that no motion has occurred within the current cell. Therefore, in step 716, the image information for the current cell in the current frame is copied into the corresponding cell of the reference frame. In step 718, the RetainStartTime parameter is updated to the current time stamp TS for the current cell, and all of its children. In step 720, the NO MOTION status is returned for use as shown in FIG. 8. The NO MOTION status indicates that there has not been any motion detected within the cell for a sufficiently long period of time.
- step 714 If in step 714, it is determined that the difference between the current time stamp TS and the RetainStartTime is not greater than the RetentionThreshold, the PENDING status is returned in step 722, and no changes are made to the retention parameters for motion tests in subsequent cycles.
- the PENDING status indicates that there is an object (or portion thereof) in the cell that is moving very slowly such that it cannot be said that no motion occurred in the cell, or that it has experienced motion in the recent past but has started to show signs of stillness.
- EdgeThreshold Used by the update edges module Used by the update edges module.
- PrctThreshold The percent threshold used by the motion status module to register a difference between corresponding cells in the current frame and reference frame.
- AmpThreshold The amplitude threshold used in comparing pixels in the current frame with pixels in the reference frame for motion.
- BlobOverlapThres Threshold used to determine if a cell has image blobs.
- BGDifffhreshold Threshold for detecting noise or background difference inside a cell.
- RetentionThreshold Time threshold (msec) for detecting a still object in a cell.
- UpdatePeriod Period at which background adaptation is called (number of frames).
- the background adaptation system and method according to the present invention There are several advantages of the background adaptation system and method according to the present invention. Changing areas of a background image are updated while areas that are not changed are not affected (not updated). This process provides for a more effective local adaptation for those regions of the background image experiencing changes.
- slow-moving objects are distinguished from background entities, and the slow moving objects are not immediately captured as part of the background image.
- the system and method according to the present invention detect significant changes in the scene, such as those caused by lighting changes, changes in the camera position or other environmental changes.
- the frequency of adaptation is adjustable to optimize the use of computation resources. It is possible to call the background adaptation process at intervals of video frames without a significant impact on performance.
Abstract
Description
______________________________________ # of Size # of Cells Total Level (pixels) Children (at Current Level) ______________________________________ 0 320 × 240 4 1 1 160 × 120 4 4 2 80 × 60 4 16 3 40 × 30 4 64 4 20 × 15 0 256 ______________________________________
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